Distributed Cooperative Tracking Control Strategy for Virtual Coupling Trains: An Event-Triggered Model Predictive Control Approach

被引:1
|
作者
Li, Zhongqi [1 ,2 ]
Zhong, Lingyu [1 ,2 ]
Yang, Hui [1 ,2 ]
Zhou, Liang [1 ,2 ]
机构
[1] East China Jiaotong Univ, Sch Elect & Automat Engn, Nanchang 330013, Peoples R China
[2] East China Jiaotong Univ, State Key Lab Performance Monitoring & Protecting, Nanchang 330013, Peoples R China
基金
中国国家自然科学基金;
关键词
virtual coupling train; dual leader topology; collaborative tracking; distributed model predictive control; event trigger conditions; stability;
D O I
10.3390/pr11123293
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Virtual coupling (VC) technology has received much attention because of its significant advantages in enhancing the railway transport capacity; it achieves efficient train coupling operation through advanced communication technology. However, due to the uncertainty of the operating environment, a stable and effective control system is the key enabler for realization. In this paper, an event-triggered distributed model predictive control (ET-DMPC) method is proposed for the cooperative tracking control of virtual coupling trains (VCTS), considering resource limitations and multiple constraints. Firstly, a distributed model predictive control (DMPC) framework is designed. Based on the established VCTS dynamics model of the dual-leader communication topology, a distributed optimization objective function and safety constraints containing state information of the neighboring train system are constructed. Secondly, due to the limitations of communication and computational resources, the event triggering (ET) mechanism is further introduced, and an ET-DMPC method suitable for VCTS is proposed. The trigger condition of each unit train is designed on the premise of guaranteeing system stability, under which the system can guarantee the input-state stability (ISS), and the recursive feasibility of the system is proven via theoretical analysis. Finally, the VCTS composed of four CRH380A unit trains is used as the control object for simulation experiments, and through two sets of experimental simulation analysis, the effectiveness of the proposed method is verified.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] Robust Event-Triggered Model Predictive Control for Multiple High-Speed Trains With Switching Topologies
    Zhao, Hui
    Dai, Xuewu
    Zhang, Qi
    Ding, Jinliang
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (05) : 4700 - 4710
  • [42] Predictive triggered control for networked control systems with event-triggered mechanism
    Wei Fu
    Simon X. Yang
    Chuanteng Huang
    Guoquan Liu
    [J]. Cluster Computing, 2019, 22 : 10185 - 10195
  • [43] Cooperative path following of constrained autonomous vehicles with model predictive control and event-triggered communications
    Hung, Nguyen T.
    Pascoal, Antonio M.
    Johansen, Tor A.
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2020, 30 (07) : 2644 - 2670
  • [44] Cooperative path following of constrained autonomous vehicles with model predictive control and event-triggered communications
    Hung, Nguyen T.
    Pascoal, Antonio M.
    Johansen, Tor A.
    [J]. International Journal of Robust and Nonlinear Control, 2020, 30 (07): : 2644 - 2670
  • [45] Predictive triggered control for networked control systems with event-triggered mechanism
    Fu, Wei
    Yang, Simon X.
    Huang, Chuanteng
    Liu, Guoquan
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 4): : S10185 - S10195
  • [46] Event-triggered model predictive schemes for freeway traffic control
    Ferrara, Antonella
    Sacone, Simona
    Siri, Silvia
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2015, 58 : 554 - 567
  • [47] Event-triggered model predictive control of wastewater treatment plants
    Du, Shengli
    Zhang, Qingda
    Han, Honggui
    Sun, Haoyuan
    Qiao, Junfei
    [J]. JOURNAL OF WATER PROCESS ENGINEERING, 2022, 47
  • [48] Mesh refinement for event-triggered nonlinear model predictive control
    Faqir, Omar J.
    Kerrigan, Eric C.
    [J]. IFAC PAPERSONLINE, 2020, 53 (02): : 6516 - 6521
  • [49] Event-triggered intermittent sampling for nonlinear model predictive control
    Hashimoto, Kazumune
    Adachi, Shuichi
    Dimarogonas, Dimos V.
    [J]. AUTOMATICA, 2017, 81 : 148 - 155
  • [50] An event-triggered Model Predictive Control scheme for freeway systems
    Ferrara, Antonella
    Oleari, Alberto Nai
    Sacone, Simona
    Siri, Silvia
    [J]. 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 6975 - 6982